How climate scientists test, test again, and use their simulation tools.

Talk to someone who rejects the conclusions of climate science and you’ll likely hear some variation of the following: “That’s all based on models, and you can make a model say anything you want.” Often, they'll suggest the models don't even have a solid foundation of data to work with—garbage in, garbage out, as the old programming adage goes. But how many of us (anywhere on the opinion spectrum) really know enough about what goes into a climate model to judge what comes out?

Climate models are used to generate projections showing the consequences of various courses of action, so they are relevant to discussions about public policy. Of course, being relevant to public policy also makes a thing vulnerable to the indiscriminate cannons on the foul battlefield of politics.

Skepticism is certainly not an unreasonable response when first exposed to the concept of a climate model. But skepticism means examining the evidence before making up one’s mind. If anyone has scrutinized the workings of climate models, it’s climate scientists—and they are confident that, just as in other fields, their models are useful scientific tools.

It’s a model, just not the fierce kind

Climate models are, at heart, giant bundles of equations—mathematical representations of everything we’ve learned about the climate system. Equations for the physics of absorbing energy from the Sun’s radiation. Equations for atmospheric and oceanic circulation. Equations for chemical cycles. Equations for the growth of vegetation. Some of these equations are simple physical laws, but some are empirical approximations of processes that occur at a scale too small to be simulated directly.

Cloud droplets, for example, might be a couple hundredths of a millimeter in diameter, while the smallest grid cells that are considered in a model may be more like a couple hundred kilometers across. Instead of trying to model individual droplets, scientists instead approximate their bulk behavior within each grid cell. These approximations are called “parameterizations.”

Connect all those equations together and the model operates like a virtual, rudimentary Earth. So long as the models behave realistically, they allow scientists to test hypotheses as well as make predictions testable by new observations.

Some components of the climate system are connected in a fairly direct manner, but some processes are too complicated to think through intuitively, and climate models can help us explore the complexity. So it's possible that shrinking sea ice in the Arctic could increase snowfall over Siberia, pushing the jet stream southward, creating summer high pressures in Europe that allow India’s monsoon rains to linger, and on it goes… It's hard to examine those connections in the real world, but it's much easier to see how things play out in a climate model. Twiddle some knobs, run the model. Twiddle again, see what changes. You get to design your own experiment—a rare luxury in some of the Earth sciences.

Enlarge/ Diagram of software architecture for the Community Earth System Model. Coupled models use interacting components simulating different parts of the climate system. Bubble size represents the number of lines of code in each component of this particular model.

In order to gain useful insights, we need climate models that behave realistically. Climate modelers are always working to develop an ever more faithful representation of the planet’s climate system. At every step along the way, the models are compared to as much real-world data as possible. They’re never perfect, but these comparisons give us a sense for what the model can do well and where it veers off track. That knowledge guides the use of the model, in that it tells us which results are robust and which are too uncertain to be relied upon.

Andrew Weaver, a researcher at the University of Victoria, uses climate models to study many aspects of the climate system and anthropogenic climate change. Weaver described the model evaluation process as including three general phases. First, you see how the model simulates a stable climate with characteristics like the modern day. “You basically take a very long run, a so-called ‘control run,'” Weaver told Ars. “You just do perpetual present-day type conditions. And you look at the statistics of the system and say, 'Does this model give me a good representation of El Niño? Does it give me a good representation of Arctic Oscillation? Do I see seasonal cycles in here? Do trees grow where they should grow? Is the carbon cycle balanced?'”

Next, the model is run in changing conditions, simulating the last couple centuries using our best estimates of the climate “forcings” (or drivers of change) at work over that time period. Those forcings include solar activity, volcanic eruptions, changing greenhouse gas concentrations, and human modifications of the landscape. “What has happened, of course, is that people have cut down trees and created pasture, so you actually have to artificially come in and cut down trees and turn it into pasture, and you have to account for this human effect on the climate system,” Weaver said.

The results are compared to observations of things like changing global temperatures, local temperatures, and precipitation patterns. Did the model capture the big picture? How about the fine details? Which fine details did it simulate poorly—and why might that be?

Enlarge/ Comparison of observed (top) and simulated (bottom) average annual precipitation between 1980 and 1999.

At this point, the model is set loose on interesting climatic periods in the past. Here, the observations are fuzzier. Proxy records of climate, like those derived from ice cores and ocean sediment cores, track the big-picture changes well but can’t provide the same level of local detail we have for the past century. Still, you can see if the model captures the unique characteristics of that period and whatever regional patterns we’ve been able to identify.

This is what models go through before researchers start using them to investigate questions or provide estimates for summary reports like those produced for the Intergovernmental Panel on Climate Change.

469 Reader Comments

I work everyday on numerical simulations in particle physics. The simulations are first-principles, they rely on very few parameters, each of which have to be controlled very carefully, otherwise conclusions can go right off the rails. It is very complicated to make even the simplest (in terms of mathematical description) models robust enough, and results are not always conclusive.

When the system gets a little bit more complicated, then the lines between conclusion, speculation, fantasy and wishful thinking becomes blurry, if not nonexistent. Take even the very definite problem of finding what is the shape of a molecule with a desired functionality, the holy grail of pharmaceutical sciences. Or the problem of protein folding. These are herculeous tasks albeit being well defined mathematically and with a controllable number of parameters.

Climate science has nothing of this. The number of parameters is huge, unknown and uncontrollable, their weight is speculative even in the simplest cases (which is more important: greenhouse gases or the sun?), input data is far from representative, and the dynamics is vastly more complicated than any other lab system that can be thought of. The huge number of free parameters is the main problem, obviously; with the right tuning, you can output lasagna. So it is very hard for me to believe any of the big claims coming from that field, especially when they are charged with politics and policy, as they often do.

And yet, the Ideal Gas MODEL does a very good job of determining the pressure of gases for a given volume, and density over a fairly wide range of temperatures. And it does this without calculating the motion of every single gas molecule.

How can this be? Because you can often derive empirical relationships for the statistical behavior of large groups of objects.

However, the fact that you you think climate modelers haven't pinned down the weight of parameters such as the forcing from the incident radiation of the sun makes it clear that you don't know what the hell you are talking about.

Cue the flood of libertarian white male IT neckbeards to tell us how AGW is fake and a plot by big science.

Yep, they're here to tell us how models suck.Then they shall depart to proselytise the absolute truth of their free-market laissez-faire insights, all the while oblivious to the fact that their truths are in fact models. They will also fail to appreciate that these economic models are themselves founded on a a series of a priori assumptions. They will parrot these assumptions as if they were fact rather than simplifications required to make numerical predictions. In not understanding that these 'truths' are in fact assumptions, they will never reflect upon how many of these simplifications are manifestly unsubstantiated in the real world.

I work everyday on numerical simulations in particle physics. The simulations are first-principles, they rely on very few parameters, each of which have to be controlled very carefully, otherwise conclusions can go right off the rails. It is very complicated to make even the simplest (in terms of mathematical description) models robust enough, and results are not always conclusive.

When the system gets a little bit more complicated, then the lines between conclusion, speculation, fantasy and wishful thinking becomes blurry, if not nonexistent. Take even the very definite problem of finding what is the shape of a molecule with a desired functionality, the holy grail of pharmaceutical sciences. Or the problem of protein folding. These are herculeous tasks albeit being well defined mathematically and with a controllable number of parameters.

Climate science has nothing of this. The number of parameters is huge, unknown and uncontrollable, their weight is speculative even in the simplest cases (which is more important: greenhouse gases or the sun?), input data is far from representative, and the dynamics is vastly more complicated than any other lab system that can be thought of. The huge number of free parameters is the main problem, obviously; with the right tuning, you can output lasagna. So it is very hard for me to believe any of the big claims coming from that field, especially when they are charged with politics and policy, as they often do.

And yet, the Ideal Gas MODEL does a very good job of determining the pressure of gases for a given volume, and density over a fairly wide range of temperatures. And it does this without calculating the motion of every single gas molecule.

How can this be? Because you can often derive empirical relationships for the statistical behavior of large groups of objects.

However, the fact that you you think climate modelers haven't pinned down the weight of parameters such as the forcing from the incident radiation of the sun makes it clear that you don't know what the hell you are talking about.

Period.

But obviously, since you work with numbers, you think you do.

There's nothing empirical about the ideal gas model. It can be rigorously derived from a very small number of microscopic assumptions, that can be easily validated in experiment.

The ideal gas model can easily be derived from a statistical treatment of a large number of free particles, with very simple kynetical laws (no dynamics). The word 'model' comes from the assumption/approximation that those particles are free, which is a good approximation for a wide range of the very few parameters that the system depends on. But there you have complete control over all parameters of the model, the microscopic and macroscopic laws are known and easily derived, and so it is completely unlike a climate model.

Now couple nonlinearly your ideal gas to an atmosphere, oceans with currents, a sun, biological agents, and thousands of other stuff you know and don't know and then give me the equivalent to the ideal gas model, and I'll be happy.

Expressing (and validating) even a significant fraction of the atmospheric model in terms of, e.g., Navier-Stokes is presently beyond our mathematical prowess. Setting aside the astronomical computational resources needed to model climate physically, the equations themselves get horrifically coupled, nonlinear, chaotic, and essentially unsolvable (at the level of detail we'd like to solve them) very quickly. Thus we take huge numerical shortcut approximations, which in turn necessitate this sort of hand-tuning. To say "the vast bulk of what a climate model is doing IS in fact basic physics" is simply false.

No, the primary flow patterns ARE described by the Navier-Stokes equations, the equations being discretised into managable grids. The basic equations themselves will set up flow patterns similar to those we see on Earth without any hand tuning at all.

I wrote the global temperature portion of an econo-energy model for the University of Illinois. Our biggest unknown was future political policy. A general outline of near-term temperature change (<30 years) didn't have a large deviation if today's current policies were used. However, when we changed emission (carbon/methane/aerosols/etc) policies slightly, the model would relax to an entirely new path. One of the largest unknowns is us.

This is exactly the point! They are the best way of predicting trends in climate, nonetheless they fail.

What is your alternative? Gut feeling?

The reality is we have to make policy decisions based on what we think our climate will do in the future. We can either say: "Let's look at the best models available from the scientific community", or we can say: "I can feel it in my gut, everything will probably be fine".

Which is the better approach to predicting climate behaviour?

Even saying "Let's wait until the models are more certain" is making a decision to delay action. Before you make that decision, you had better have a whole lot of evidence on hand to justify ignoring the best models science has to offer.

I wrote the global temperature portion of an econo-energy model for the University of Illinois. Our biggest unknown was future political policy. A general outline of near-term temperature change (<30 years) didn't have a large deviation if today's current policies were used. However, when we changed emission (carbon/methane/aerosols/etc) policies slightly, the model would relax to an entirely new path. One of the largest unknowns is us.

Scientific models with political policy parameters? I rest my case...

It was actually very exciting research. The model allows legislators to quickly see the potential results of implementing policy. This type of modeling is imperative for making smart decisions for our future.

Experts who have worked on the problem for decades, presumably part of a self correcting community who are literally the only ones who could model and simulate the problem, flippantly dismissed as not knowing what they are doing.

People who really know what they are doing freely admit the problems and weaknesses of their models, design, code, their work. That they admit it doesn't mean they don't know anything. It means they aren't dogmatic like fanatics are and will self correct when better models come out. Their opinion is really the only one that matters as at least their guess is an educated one. Everyone else really doesn't know anything.

Some of the comments read too much like criticisms of evolution, cosmology, almost any field. The model makes quite good predictions, but you're throwing everything away and calling it junk when it's not accurate to the 10th decimal point, especially when all that matters is accuracy to the 2nd decimal point.

Some of the comments read too much like criticisms of evolution, cosmology, almost any field. The model makes quite good predictions, but you're throwing everything away and calling it junk when it's not accurate to the 10th decimal point, especially when all that matters is accuracy to the 2nd decimal point.

It's not even that. They don't know the science necessary to really understand the matter (neither do I, frankly), but they read a summary, and think that they spot flaws, and declare the whole thing rubbish - when they haven't actually identified any actual flaws in the science.

I work everyday on numerical simulations in particle physics. The simulations are first-principles, they rely on very few parameters, each of which have to be controlled very carefully, otherwise conclusions can go right off the rails. It is very complicated to make even the simplest (in terms of mathematical description) models robust enough, and results are not always conclusive.

When the system gets a little bit more complicated, then the lines between conclusion, speculation, fantasy and wishful thinking becomes blurry, if not nonexistent. Take even the very definite problem of finding what is the shape of a molecule with a desired functionality, the holy grail of pharmaceutical sciences. Or the problem of protein folding. These are herculeous tasks albeit being well defined mathematically and with a controllable number of parameters.

Climate science has nothing of this. The number of parameters is huge, unknown and uncontrollable, their weight is speculative even in the simplest cases (which is more important: greenhouse gases or the sun?), input data is far from representative, and the dynamics is vastly more complicated than any other lab system that can be thought of. The huge number of free parameters is the main problem, obviously; with the right tuning, you can output lasagna. So it is very hard for me to believe any of the big claims coming from that field, especially when they are charged with politics and policy, as they often do.

And yet, the Ideal Gas MODEL does a very good job of determining the pressure of gases for a given volume, and density over a fairly wide range of temperatures. And it does this without calculating the motion of every single gas molecule.

How can this be? Because you can often derive empirical relationships for the statistical behavior of large groups of objects.

However, the fact that you you think climate modelers haven't pinned down the weight of parameters such as the forcing from the incident radiation of the sun makes it clear that you don't know what the hell you are talking about.

Period.

But obviously, since you work with numbers, you think you do.

There's nothing empirical about the ideal gas model. It can be rigorously derived from a very small number of microscopic assumptions, that can be easily validated in experiment.

The ideal gas model can easily be derived from a statistical treatment of a large number of free particles, with very simple kynetical laws (no dynamics). The word 'model' comes from the assumption/approximation that those particles are free, which is a good approximation for a wide range of the very few parameters that the system depends on. But there you have complete control over all parameters of the model, the microscopic and macroscopic laws are known and easily derived, and so it is completely unlike a climate model.

Now couple nonlinearly your ideal gas to an atmosphere, oceans with currents, a sun, biological agents, and thousands of other stuff you know and don't know and then give me the equivalent to the ideal gas model, and I'll be happy.

Sure, it can be derived from 'first principles' if you ignore the physical reality of the particles, approximate them as points, and completely wash out their dynamics. And, since it's a simple system, this approximation tends to work ok over a pretty large range (though not everywhere).

Or, it can be _EMPIRICALLY_ derived from the combined gas law and Avagadro's law.

But of course the simulation of the climate is a lot more complicated, which is why you don't end up with a simple equation like the ideal gas law. That doesn't mean that the scientific process isn't the same, it just means ti's a lot harder for non-specialists to know what's going on, and leads many to say stupid things like climate modelers don't know how to account for the incident energy of the sun.

The fact that you want an 'ideal gas law' for such a complex system again displays your inability to appreciate what happens when science gets complex. Do you ignore any science that doesn't boil down to a simple, complete equation? Have you thrown out the Standard model because it's incomplete, or is it 'good enough' to apply in the domain of interest?

Heck, even the ideal gas law breaks down under many conditions, yet you hold it up as a 'good model', which it is as long as you apply it to the appropriate domain with appropriate caveats...just like climate models.

I wrote the global temperature portion of an econo-energy model for the University of Illinois. Our biggest unknown was future political policy. A general outline of near-term temperature change (<30 years) didn't have a large deviation if today's current policies were used. However, when we changed emission (carbon/methane/aerosols/etc) policies slightly, the model would relax to an entirely new path. One of the largest unknowns is us.

Scientific models with political policy parameters? I rest my case...

You're just trolling us, right? Come on, no one can be that stupid, right?

You have to account for human behaviors that impact the system being modeled. If you don't, you aren't actually accounting for all the major factors determining the state of the system.

On a side note, the amount of bald-faced, head-in-the-ground density of the initial comment flood is making me very sad.

I wrote the global temperature portion of an econo-energy model for the University of Illinois. Our biggest unknown was future political policy. A general outline of near-term temperature change (<30 years) didn't have a large deviation if today's current policies were used. However, when we changed emission (carbon/methane/aerosols/etc) policies slightly, the model would relax to an entirely new path. One of the largest unknowns is us.

Scientific models with political policy parameters? I rest my case...

You're just trolling us, right? Come on, no one can be that stupid, right?

You have to account for human behaviors that impact the system being modeled. If you don't, you aren't actually accounting for all the major factors determining the state of the system.

On a side note, the amount of bald-faced, head-in-the-ground density of the initial comment flood is making me very sad.

It's not unexpected. The tech community tends to run pretty libertarian, and AGW is such an extreme and inescapable example of the Tragedy of the Commons that it's extremely hard to incorporate a self-consistent solution into a libertarian philosophy. Denial is the mentally easier resolution to the cognitive dissonance.

I am not sure why the fact that a model backcasts well should tell me anything about how well it will predict the future. How well a model predicts the future, before the fact, is really the only metric of how well a model can predict the future. I get that backcasting is useful for validating the model's physics in general, but it tells us nothing about how well it will predict the future.

I note there is no mention about how poorly past models have faired in predicting the last decade's temperature trends. Perhaps those past models weren't very good and today's are better - I look forward to seeing how well current models predict the next few decades.

Did you skip the part where it clearly stated that part og the testing is letting it run and see how far it deviates from reality each day? That IS testing it against the future

Cue the flood of libertarian white male IT neckbeards to tell us how AGW is fake and a plot by big science.

Yep, they're here to tell us how models suck.Then they shall depart to proselytise the absolute truth of their free-market laissez-faire insights, all the while oblivious to the fact that their truths are in fact models. They will also fail to appreciate that these economic models are themselves founded on a a series of a priori assumptions. They will parrot these assumptions as if they were fact rather than simplifications required to make numerical predictions. In not understanding that these 'truths' are in fact assumptions, they will never reflect upon how many of these simplifications are manifestly unsubstantiated in the real world.

I think that there's some good news, though:

The "debate" is still going hot and heavy, but (by my admittedly non-scientific estimation of what I've been seeing seeing) the actual action is moving on.

The faux-sceptic Climate Change/AGW deniers are being left behind as society finally absorbs and adapts to the new reality, and those who so vociferously try to "enlighten" the general public are already well on their way to being regarded as being little different than the 9/11 "truthers", the "chem-trails" crowd, the well-meaning but misinformed "vaccine causes autism" activists, the alien-abduction crowd or the Chariots of the Gods believers, etc, etc. Their beliefs and claims might be entertaining, but don't merit serious consideration any longer -- as we have real world concerns to actually deal with.

Even in US Republican and "conservative" circles, discussion is finally starting to gingerly address the ticklish question of whether, just maybe, it just might be time to implement some sort of "revenue-neutral" carbon tax.

Thank you for an excellent article. I liked the quote that all models are wrong, but that some are useful. Civil and Structural engineering also rely on models, and none of them are able to predict the exact way that a bridge or building will behave in use. Soil models are even more inexact, because of the inherent variability of the materials. Yet most city-dwellers will rely upon these models and upon the judgment of the engineers that use them, because they are good enough, in most cases. And yet they are also constantly being improved on as we seek to do more while trying to manage the risk. These are essentially self-governed communities, because those outside do not understand the principles and methodology. I see a clear parallel here.

The parallel is not all that clear. Engineering models make heavy use of the adage, "when in doubt, make it stronger".

Not to mention that civil engineering is a highly localized activity. Building a house is pretty easy. A skyscraper, harder. A long span bridge, harder still. A tunnel underneath the English Channel, super-duper hard. A clear parallel to a global climate model would be to build a continuous bridge/tunnel system that circumnavigates the Earth. We've got a few thousand years of experience in building stuff, yet I doubt you could get a single civil engineer to agree that it could be done. It's too big, and there are too many unknown unknowns.

Which is not to say that climate models are useless. I don't think they should all be shitcanned. Actually, I think it would be impossible to do so--it's a vital and compelling corner of science, and the work done there is quite valuable.

I wouldn't hang a man on the evidence presented by climate models, but I would give him a stern talking to.

The overall climate models formed from the aggregation of these smaller models are some of the most complex software in existence today, and represent the best current understanding we have of our climate systems. There simply isn't any better way of predicting trends in climate.

This is exactly the point! They are the best way of predicting trends in climate, nonetheless they fail.

Actually they don't "fail," they do a pretty damn good job. This indicates that, while we don't yet have the best possible models for the various parts of the climate system, we have damn good ones that are more than sufficient to start incorporating into our policy decisions.

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It should be common sense to avoid any big claims before they reach an acceptable level of control over the problem at hand, and before it is tested exhaustively.

Did you somehow miss all the parts in the article that talked about the exhaustive testing of the models? Against time periods where we have a ton of empirical data for almost every metric, against time periods where were have only specific kinds of empirical data, and against each other.

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Even in particle physics, whenever some effect is confirmed with less than 99.7% confidence level, serious physicists call it a rumour, and/or a hype (if it becomes trendy).

You are talking about a fundamentally different kind of confirmation from a different branch of science that has its own (different) standards, methods, constraints, and challenges. High energy physics might require 5-sigma before declaring something officially "discovered," but many other sciences do not. One of the most common, for example, is merely 2 sigma (a confidence interval greater than about 95%). This does not mean that the other fields are somehow less scientific. But good job perpetuating the stereotype that physicists think they can barge into other fields and tell the seasoned specialists how to do their own jobs.

I wrote the global temperature portion of an econo-energy model for the University of Illinois. Our biggest unknown was future political policy. A general outline of near-term temperature change (<30 years) didn't have a large deviation if today's current policies were used. However, when we changed emission (carbon/methane/aerosols/etc) policies slightly, the model would relax to an entirely new path. One of the largest unknowns is us.

Scientific models with political policy parameters? I rest my case...

Which just shows that you're not wearing the appropriately sized britches, son.

The "political policy parameters" are the basis of different model scenarios used to project future conditions if some factors are assumed to play out a certain way going forward. These different scenarios represent what can happen if we were to cut carbon emissions in 30 years versus 75, for example. Scenario A might be to assume that for the 21st century, consumption of fossil fuels continues with the same patterns in the future as we've observed playing out over the last century. It would take into account trends in consumption for different regions, fluctuating standards of living, and population growth as well as available/potential reserves of different fuel sources. Scenario B might be to assume that in a few decades we make a concerted effort to shift towards non-carbon sources of energy. Scenario C might be to assume that consumption of fossil fuels would remain fixed at 2012 levels going forward, as a kind of "control" or baseline for comparing these different policy decisions and their impact on the climate.The IPCC's scenarios and their selection process for the previous assessment report are outlined here. The new process, for the upcoming 5th assessment report, are outlined here. So it's pretty telling that you've singled this bit about political parameters out as if it proved your point. They are not the kind of parameterizations you seem to have such an issue with regarding the tunability of the models themselves. They are the hypothetical scenarios under which the underlying models play out. You can't have a climate model without scenarios, and in this case the impacts of policy decisions directly affects the outcome not because the model itself has been changed, but because the inputs are examined for different values reflecting those policy decisions. You don't even understand this much, yet you presume to make sweeping pronouncements about the scientific validity of climate modeling?

A new paper by prominent German climatologists Dr. Hans von Storch and Dr. Eduardo Zorita, et al, finds "that the continued [global] warming stagnation over fifteen years, from 1998 -2012, is no longer consistent with model projections even at the 2% confidence level."

As with all of the rest of your points and links, it's an egregiously deliberate misrepresentation to make a scientist who makes his living studying global warming (and who supports the very things you're trying to say he denies) out to be an AGW denialist.

Hi, thanks. The reason I didn't read them was because you provided a link to a propaganda blog rather than the reference to a scientific article. I've had a read of them now, after finding the original articles, without the selective highlighting done by the propaganda blog, as listed here (I hope the links work):

I would like to point out that von Storch and Zorita (authors on one of the papers) specifically say "To understand the present mismatch [between observations and model results], we suggest four different explanations; none is pointing to a falsification of the concept that CO2 and other greenhouse gases exert a strong and likely dominant influence on the climate (statistics of weather). None represents a falsification of climate models." I found that in a follow-up letter here: http://klimazwiebel.blogspot.com.au/201 ... .html#more.

There is a difference between what the model is predicting and observations over the last 15 years. This does not mean that the whole model is "falsified". The model might be wrong (by some amount with some level of certainty) in terms of this particular quantity, but it is not "falsified" in its entirety. That was my objection in the first place. Your terminology is sensationalist and incorrect.

To all of you advocating doing nothing about climate change because we simply don't have enough data or not convincing enough data etc.: Why is that a rational approach? To me it seems that it would be much more rational to treat it as an insurance problem.

How sure are you that your house is not going to burn down today, during the next week or during the next year? How sure are you that you won't get sick, have an accident, face a tornado, earthquake or any other of the gazillion risks you can buy insurance for?

You don't really know exactly, but most of us do buy insurance nevertheless, i.e. we put down some small percentage of our income to ward off potential future catastrophes. If your home is on a low beach in the path of many hurricanes you would probably buy better flood insurance, while if you live on top of the tallest mountain in the middle of a desert you probably don't buy any. Your decision depends on your perception of the risk.

Why is the same thinking not applied to climate topics? Even if you believe that climate science is mostly bogus, how much is that "mostly" exactly? If you are 95% sure that no warming is going to happen, you should still "insure" that 5% risk that you are wrong. Anything else would be irrational.

Think of these problems not as a "this is clearly wrong" or "this is clearly right" but as a "how confident am I that these results are right / wrong". Incidentally this would put you much more in line with actual scientific thinking, the IPCC reports are in fact nothing but attempts to quantify our uncertainty on future climate.

Try to do the math. Pick any insurance you have against reasonably unlikely things, and look up how much you spend for that. Now try to ballpark how that compares against your certainty/uncertainty of rising temperatures and their effect they may have on you (either directly or indirectly via e.g. economic up/downturns). Assuming the result is nonzero and you are still opposing any mitigating action, how do you rationally reconcile that?

one issue skirted in this writeup is that these models are not, primarily, physical models. They're not bottom-up physical simulations where we can just put in initial conditions and get a result. Rather, they're numerical models that are painstakingly hand-crafted to produce answers that look reasonable and consistent with what we see, and with other models.

Paramaterisation was covered quite well in the article, I thought.

Paramaterisation occurs for specific physical processes, where a more extensive simulation is computationally prohibitive.

Certainly. And there's nothing wrong with using this technique. But is paramaterisation a "bottom-up physical simulation?" No, and it doesn't look like you're claiming that. Is it entirely a "numerical model"? Well, no, it's not that either. But I'd say it's much closer to a numerical model than physical.

A good rule of thumb for evaluating where models are on the 'bottom-up physical sim' vs 'numerical approximation' continuum is whether (or, realistically, how many) additional terms are added, and how much data manipulation goes on, at each level. A true bottom-up grid paramaterisation wouldn't involve any new terms or 'massaging' at each larger scale, but I'm certain current climate models are very far from this. Beyond this, I guess it's a definitional nitpick, but I know there's a vast amount of numerical approximations and massaging going on, and feel very confident about calling climate models "not bottom-up physical models".

Expressing (and validating) even a significant fraction of the atmospheric model in terms of, e.g., Navier-Stokes is presently beyond our mathematical prowess. Setting aside the astronomical computational resources needed to model climate physically, the equations themselves get horrifically coupled, nonlinear, chaotic, and essentially unsolvable (at the level of detail we'd like to solve them) very quickly. Thus we take huge numerical shortcut approximations, which in turn necessitate this sort of hand-tuning. To say "the vast bulk of what a climate model is doing IS in fact basic physics" is simply false.

No, the primary flow patterns ARE described by the Navier-Stokes equations, the equations being discretised into managable grids. The basic equations themselves will set up flow patterns similar to those we see on Earth without any hand tuning at all.

We can both be factually correct here, though I think it's misleading to suggest "the vast bulk of what a climate model is doing IS in fact basic physics." I'm sure Navier-Stokes and other physical equations are integrated with the models, but this does not make them bottom-up physical models, much as I can model bacterial immunity with evolutionary algorithms, yet doing so doesn't make my model a bottom-up evolutionary model.

Some people are not willing to go beyond the "hard" math they learned. Black and white, but mostly shades of grey with dithering.

Here's something that blew my mind: how to approximate Pi with gravity, a wooden floor and pins!Buffon's needle, it's not extremely precise but the crudeness/weirdness to actual results ratio make a point.

The weird thing I don't get about anti-AGW people is what they think the researchers' motives are.

Hypothesis 1: AGW researchers have discovered something real and scary, and are trying their best to learn about itReal-world match: Pretty good. Research is messy, and there is debate, conflicting theories, and new discoveries along the way. They do a very good job looking like actual researchers.

Hypothesis 2: AGW researchers are in it for the moneyReal-world match: Terrible. Academic money isn't good, and you have to deal with your particular field getting absolutely torn apart and hated by a huge fraction of the US population. You can also probably make a TON more money working for an oil/gas/coal company as a denying mouthpiece, or your modelling skills are probably useful generating proprietary stuff for the military or an insurance company (two organizations that have bought into warming because they have to be based in reality)

Hypothesis 3: AGW researchers are in it because they simply hate progressReal-world match: This doesn't make a lot of sense. At some point, there has to be some human want or need fulfilled. If they hate progress, why aren't there similarly noisy people whining about computers, space exploration, or cars? It seems odd that a big fraction of academia would all latch onto CO2 - we output lots of other gases and chemicals, why not jump onto them too, and try to push for international agreements to limit them? If CO2 isn't harmful, then there should be a big push to limit lots of other harmless outputs.

Hypothesis 4: AGW researchers are in it for the political powerReal-world match: What political power? They chose research that directly pushes against the largest and richest companies on the planet. If you were to make up some topic just to gain political power, I'm sure you'd pick something with a less-powerful opponent. For politicians already in power, it is far easier to gain power by simulating a terrorist attack or starting a war.

Also, I think every global-warming article should end with:

Quote:

And whether you believe AGW is happening, the oceans are still being acidified. I hope you don't enjoy eating anything that eats plankton.

So you start with these equations, and you start these equations with a world that has no moisture in the atmosphere that just has seeds on land but has no trees anywhere, that has an ocean that has a constant temperature and a constant amount of salt in it, and it has no sea ice, and all you do is turn it on. [Flick on] the Sun, and you see this model predict a system that looks so much like the real world. It predicts storm tracks where they should be, it predicts ocean circulation where it should be, it grows trees where it should, it grows a carbon cycle—it really is remarkable.”

Anyone who is interested in a realistic assessment of the ability to use computers to model complex phenomena should consider the history of efforts to model the folding of proteins. Compared to the climate, protein folding is a very well defined and limited problem. It is also a case where many right answers are known and can be used to help create models realistic enough to be accurate. Unlike the climate studies, the fact that right answers are known makes it possible to really know how well a protein folding model is working. In practice it has proven to be extremely difficult to figure out how a protein folds from computer analysis even thought the basic ideas about the bonds involved in folding have been understood from Linus Pauling's work in the 1950's.

So you start with these equations, and you start these equations with a world that has no moisture in the atmosphere that just has seeds on land but has no trees anywhere, that has an ocean that has a constant temperature and a constant amount of salt in it, and it has no sea ice, and all you do is turn it on. [Flick on] the Sun, and you see this model predict a system that looks so much like the real world. It predicts storm tracks where they should be, it predicts ocean circulation where it should be, it grows trees where it should, it grows a carbon cycle—it really is remarkable.”

I'd really lime to see a visualisation of one of these.

Don't worry; Peter Molyneux will build a game around the model, and when his game fails to grow more than one scraggly shrubbery, will claim it's the best game he's ever made and will revolutionize the entire known universe.